Introduction to the ImmuMicrobiome package
Rémy Villette
2025-07-16
1 Introduction
This package is based on the code of Dr. Villette and Dr. Larsen. The
package is written and maintained by Dr. Villette. This package is meant
to facilitate microbiome exploration and ensuring nice plotting.
This package covers :
The dada2 pipeline with wrapper functions that ease the processing of multiple projects
Some plotting functions for beta diversity, heatmap and differential abundance analysis using directly a phyloseq object
A pipeline for IgASeq analysis
2 Trim, denoise and align your sequences using dada2 pipeline
For convenience this will not be a reproducible example, dada2 takes too long to compute and knit. This part of the tutorial will present a run that we performed in house. The rest of the tutorial will be based on reproducible data.
2.1 Get the files
We use here a wrapper function that will create a list of three for pair end :
forward files
reverse files
names of the files
And for single end : -
a list of files
names of the files
f_list = list_fastq("/home/bigbeast/Documents/tmp/2022-12 CIMMAP run ELISE",
pattern = c("R1", "R2"), separator = "_", level = 1)
# check that all files are distinct
lapply(f_list, duplicated)
# check that all files exist5
lapply(f_list, file.exists)
random= lapply(f_list, "[",sample(1:255,30))
# tmp= summarise_fastq(random, cores =30, plot = F )2.2 Check the quality profile
We will use the function qc_check. This will take time
as the plotQualityProfile isn’t parallelized in dada2. This
function will create two plots (for pair end) of n
aggregated samples and only one plot if you are using single end.
qc_check(flist, n=30)2.3 Check the best trimming parameters for your set of fastq
2.3.1 Test optimal cutting param
set.seed(1)
tmp = lapply(f_list, "[",sample(1:255,30))
tmp_list= filt_list(tmp)
filtered= list()
cb= combn(x= (300-seq(0, 50, by=10)), m=2)
# Very long be careful
#try to test combination of trimmings
for(i in 1:dim(cb)[2]){
filtered[[i]]= filter_fastq(tmp, tmp_list, cutting_param = cb[ ,i], cores = 35, trimleft = 35, maxEE = c(3,4))
}
#extract the data to a df
per= NULL
for(i in 1:15) {
t = filtered[[i]] %>%
as.data.frame() %>%
mutate(per=reads.out/reads.in)
per= cbind(per, t$per) %>%
as.data.frame()
# per$names=rownames(tmp[1])
print(per)
}
colnames(per)= paste(cb[1,], cb[2,])
per$names=rownames(t[1])
# plot
png("transmic and IgA rescue mice percent reads passed depending on cutting parameters.png", width = 600, height=400)
per %>%
as.data.frame() %>%
pivot_longer(names_to = "cut", values_to = "per", cols = 1:15) %>%
ggplot(aes(y=per, x= cut))+
geom_boxplot()+ geom_line(aes(group=names, col=names))+
labs(y= "percentage passed", x= "Trimming parameters")+
theme(axis.text.x = element_text(angle=90), legend.position = "none")
dev.off()2.3.2 Test optimal maxEE
cb= combn(x= (7-seq(0,5, by=1)), m=2)
cb = cb[nrow(cb):1,]
# Very long be careful
#try to test combination of trimmings
# registerDoParallel(cl = makeCluster(5))
for(i in 1:dim(cb)[2]){
filtered[[i]]= filter_fastq(tmp, tmp_list, cutting_param = c(260,250),
cores = 35, trimleft = 35, maxEE = cb[ ,i])
}
#extract the data to a df
per= NULL
for(i in 1:15) {
t = filtered[[i]] %>%
as.data.frame() %>%
mutate(per=reads.out/reads.in)
per= cbind(per, t$per) %>%
as.data.frame()
}
colnames(per)= paste(cb[1,], cb[2,])
per$names=rownames(t[1])
# plot
png("transmic and IgA rescue mice percent reads passed depending on errors parameters.png", width = 600, height=400)
per %>% as.data.frame() %>% pivot_longer(names_to = "cut", values_to = "per", cols = 1:15) %>%
ggplot(aes(y=per, x= cut))+
geom_boxplot()+ geom_line(aes(group=names, col=names))+
labs(y= "percentage passed", x= "Trimming parameters")+
theme(axis.text.x = element_text(angle=90), legend.position = "none")
dev.off()
2.4 Filter and trim the fastq
We now have to remove the bad quality reads and trim the length. You will find a function to create the list of filtered files and one to make the filtered files.
filt= filt_list(f_list) # create the list of filtered files
filtered = filterAndTrim(fwd= fwd, filt = filtFs, rev=rv, filt.rev = filtRs, truncLen = c(260,240), trimLeft = 25, maxEE = c(3,5), multithread = 45)3 Analyse your 16S / metabolomic data
3.1 Phyloseq object oriented functions
We will use the enterotype data to explore some of
the plotting functions. Let’s start with the beta diversity functions
beta_diversity and beta_dispersion.
data(enterotype)
3.1.1 Alpha diversity plots
Alpha diversity is an important facet of microbiome analysis. I’ve created wrapper function to plot either alpha diversity as boxplots or as line plots.
alpha_diversity(enterotype, measure="Shannon", x="Enterotype", group="SeqTech", plot_type="boxplot")
alpha_diversity(enterotype, measure="Shannon", x="Enterotype", group="SeqTech", plot_type="line")
These plots are compatible with other aspects of ggplot, such as facets or stats. Stats are also implemented in this function but are very sparse.
alpha_diversity(enterotype, measure="Shannon", x="Enterotype", group="SeqTech", plot_type="boxplot", stat = T)
library(ggpubr)
enterotype %>%
subset_samples(Enterotype!="NA")%>%
alpha_diversity( measure="Shannon", x="Enterotype", group="Enterotype", plot_type="boxplot")+
stat_compare_means(comparisons = list(c("1", "2")))+
facet_grid(~SeqTech)
3.1.2 Beta diversity plots
You will have the choice between beta_diversity and
beta_dispersion for your beta diversity plotting.
beta_dispersion will plot PCoA, NMDS, PCA, DCA, CA and
t-SNE for the moment. This function will plot the two components of your
choosing, confidence ellipses and boxplot for each axis
and for each group. Each function will return a plot and a percentage of
contribution for each component.
beta_diversity will be removed progressively from this
package. This function is redundant with
beta_dispersion.
beta_diversity(enterotype, dist="bray", method="PCoA", group="SeqTech", permanova = F)
beta_dispersion(enterotype, dist = "bray", method = "PCoA", group = "SeqTech")
#> $reduction
#> Call: vegan::wcmdscale(d = d, eig = T)
#>
#> Inertia Rank
#> Total 57.45
#> Real 69.09 114
#> Imaginary -11.63 165
#>
#> Results have 280 points, 114 axes
#>
#> Eigenvalues:
#> [1] 31.376 8.067 5.376 2.497 2.208 1.753 1.385 1.342 0.967 0.865
#> [11] 0.779 0.695 0.648 0.543 0.517 0.473 0.450 0.428 0.410 0.382
#> [21] 0.368 0.357 0.334 0.310 0.303 0.291 0.283 0.272 0.242 0.231
#> [31] 0.216 0.208 0.204 0.189 0.180 0.175 0.163 0.156 0.150 0.146
#> [41] 0.138 0.132 0.128 0.125 0.121 0.119 0.112 0.107 0.101 0.097
#> [51] 0.093 0.089 0.083 0.080 0.079 0.073 0.072 0.068 0.065 0.064
#> [61] 0.060 0.059 0.055 0.054 0.051 0.049 0.048 0.044 0.043 0.041
#> [71] 0.041 0.039 0.037 0.035 0.034 0.033 0.031 0.030 0.030 0.028
#> [81] 0.025 0.024 0.024 0.023 0.021 0.020 0.020 0.018 0.016 0.015
#> [91] 0.014 0.013 0.013 0.012 0.011 0.011 0.010 0.009 0.009 0.008
#> [101] 0.007 0.006 0.005 0.005 0.004 0.004 0.003 0.003 0.002 0.001
#> [111] 0.001 0.001 0.000 0.000 0.000 -0.001 -0.001 -0.001 -0.002 -0.002
#> (Showing 120 of 279 eigenvalues)
#>
#> Weights: Constant
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 30.538 0.53153 157.14 0.001 ***
#> Residual 277 26.915 0.46847
#> Total 279 57.453 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# beta_dispersion()3.1.2.1 Multiple dimension reductions
There are unconstrained and constrained approaches to beta diversity. Unconstrained analysis, more common in literature, are either based on:
- dissimilarity matrix (PCoA, NMDS, DCA)
- directly on the raw matrix (PCA, CA)
- t-SNE that can use both dissimilarity matrix or the raw matrix
3.1.2.1.1 Unconstrained models
You can play with the parameters of each function like the following plots :
3.1.2.1.1.1 PCoA
beta_dispersion(enterotype, dist = "bray", method = "PCoA", group = "SeqTech",
color_vector = c("#777711", "#117777", "#DD7788"),
legend_title = "Sequencing tech", lwd = 2,
font = 2, draw = "polygon", text = T, permanova = T,
y.intersp = 0.7)
#> $reduction
#> Call: vegan::wcmdscale(d = d, eig = T)
#>
#> Inertia Rank
#> Total 57.45
#> Real 69.09 114
#> Imaginary -11.63 165
#>
#> Results have 280 points, 114 axes
#>
#> Eigenvalues:
#> [1] 31.376 8.067 5.376 2.497 2.208 1.753 1.385 1.342 0.967 0.865
#> [11] 0.779 0.695 0.648 0.543 0.517 0.473 0.450 0.428 0.410 0.382
#> [21] 0.368 0.357 0.334 0.310 0.303 0.291 0.283 0.272 0.242 0.231
#> [31] 0.216 0.208 0.204 0.189 0.180 0.175 0.163 0.156 0.150 0.146
#> [41] 0.138 0.132 0.128 0.125 0.121 0.119 0.112 0.107 0.101 0.097
#> [51] 0.093 0.089 0.083 0.080 0.079 0.073 0.072 0.068 0.065 0.064
#> [61] 0.060 0.059 0.055 0.054 0.051 0.049 0.048 0.044 0.043 0.041
#> [71] 0.041 0.039 0.037 0.035 0.034 0.033 0.031 0.030 0.030 0.028
#> [81] 0.025 0.024 0.024 0.023 0.021 0.020 0.020 0.018 0.016 0.015
#> [91] 0.014 0.013 0.013 0.012 0.011 0.011 0.010 0.009 0.009 0.008
#> [101] 0.007 0.006 0.005 0.005 0.004 0.004 0.003 0.003 0.002 0.001
#> [111] 0.001 0.001 0.000 0.000 0.000 -0.001 -0.001 -0.001 -0.002 -0.002
#> (Showing 120 of 279 eigenvalues)
#>
#> Weights: Constant
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 30.538 0.53153 157.14 0.001 ***
#> Residual 277 26.915 0.46847
#> Total 279 57.453 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.2.1.1.2 t-SNE
beta_dispersion(enterotype, dist = "bray", method = "tsne", group = "SeqTech",
color_vector = c("#777711", "#117777", "#DD7788"),
legend_title = "Sequencing tech without boxplots", lwd = 2,
font = 2, draw = "polygon", text = T, permanova = T,
y.intersp = 0.7,
boxplot = F, where = "bottom")
#> $reduction
#> $N
#> [1] 280
#>
#> $Y
#> [,1] [,2]
#> [1,] -0.08402678 -14.489454
#> [2,] -0.54750203 -13.809009
#> [3,] -1.82478383 -14.755933
#> [4,] -0.06821868 -14.467274
#> [5,] 1.97026209 -19.348574
#> [6,] 0.41635562 -19.687455
#> [7,] -7.05393332 -18.784530
#> [8,] -5.59049099 -23.130444
#> [9,] -5.96256394 -22.921497
#> [10,] -4.89538333 -22.854616
#> [11,] -6.48087334 -7.842522
#> [12,] 3.47081834 -18.874737
#> [13,] 3.42235248 -17.884296
#> [14,] -4.85651732 -15.804279
#> [15,] -3.30183631 -16.409896
#> [16,] -7.34523394 -18.568468
#> [17,] -6.42980260 -10.317381
#> [18,] -3.07056440 -14.013133
#> [19,] -0.48074417 -16.770656
#> [20,] -0.23323519 -17.710207
#> [21,] -2.76930815 -14.301993
#> [22,] -4.13480760 -12.635360
#> [23,] -4.25933909 -11.718129
#> [24,] -2.29766837 -19.091395
#> [25,] -4.17630339 -11.861561
#> [26,] 4.12419628 -19.109204
#> [27,] -4.56411513 -19.341799
#> [28,] -0.37764288 -17.098845
#> [29,] -6.24206625 -8.839312
#> [30,] -1.64852069 -15.967286
#> [31,] -1.61104316 -16.035173
#> [32,] -4.73414595 -10.161157
#> [33,] -3.75770089 -18.375639
#> [34,] -6.25642453 -9.121743
#> [35,] -5.25682246 -7.877132
#> [36,] -5.93912089 -9.467616
#> [37,] -4.33381438 -10.478112
#> [38,] -2.35659164 -19.243410
#> [39,] 2.32555631 -2.603642
#> [40,] 2.32529624 -2.604949
#> [41,] -8.79902664 -15.972933
#> [42,] 4.09680036 -19.437427
#> [43,] -3.97166715 -14.769294
#> [44,] -5.44896749 -12.983928
#> [45,] -5.69132362 -16.310864
#> [46,] 2.63022031 -19.339559
#> [47,] 1.24257391 -19.345449
#> [48,] -3.05425292 -15.877069
#> [49,] -3.40616616 -11.251594
#> [50,] -4.51224603 -22.472634
#> [51,] -5.74650482 -8.759965
#> [52,] -5.74142125 -21.024695
#> [53,] -5.68222734 -22.448863
#> [54,] -6.40278029 -18.773218
#> [55,] -8.10944434 -16.050188
#> [56,] -4.61137108 -13.832931
#> [57,] -6.55183336 -11.458356
#> [58,] -4.60966241 -16.443896
#> [59,] 3.88180910 -18.659281
#> [60,] -4.74534228 -11.916155
#> [61,] -6.02941693 -10.186810
#> [62,] -4.58029687 -17.315900
#> [63,] -5.09213360 -21.819865
#> [64,] 0.75955290 -18.831250
#> [65,] -6.42385298 -14.265248
#> [66,] -6.32845661 -16.396996
#> [67,] 1.20158764 -18.975777
#> [68,] -5.05033889 -11.828210
#> [69,] -6.39584072 -16.743191
#> [70,] -4.72867266 -22.451533
#> [71,] -4.25752373 -21.949554
#> [72,] 3.25183230 -19.323963
#> [73,] -4.93930012 -14.849980
#> [74,] 3.75882295 -19.448826
#> [75,] -5.16054325 -17.660998
#> [76,] -5.47929717 -17.446213
#> [77,] -6.70663684 -15.919409
#> [78,] -6.08029840 -16.122960
#> [79,] -6.25641463 -12.659702
#> [80,] -5.98121271 -18.150946
#> [81,] -5.61614968 -11.170241
#> [82,] -5.60178851 -18.879910
#> [83,] -6.20371782 -15.442153
#> [84,] -2.49122518 -12.632536
#> [85,] -6.28501956 -12.475014
#> [86,] -0.21237170 -18.956493
#> [87,] -5.91623476 -22.768658
#> [88,] -6.02359865 -18.254761
#> [89,] 1.41890147 -18.545347
#> [90,] -1.51972426 -17.559261
#> [91,] -5.32123071 -14.971276
#> [92,] -5.37162661 -21.073610
#> [93,] -4.09705068 -21.988633
#> [94,] -5.74480072 -18.938054
#> [95,] -4.88570905 -17.793504
#> [96,] -4.99414535 -18.513430
#> [97,] -6.83182020 -17.101413
#> [98,] -5.89026559 -14.111029
#> [99,] -3.91454588 -15.576334
#> [100,] -5.52067449 -21.860965
#> [101,] -6.65644640 -10.408689
#> [102,] -2.53364750 -15.300491
#> [103,] -0.03699264 -19.860098
#> [104,] 2.08012299 -18.945190
#> [105,] -4.45404342 -21.764321
#> [106,] -5.61674115 -19.133273
#> [107,] -6.23163493 -12.879730
#> [108,] -5.88494121 -9.697062
#> [109,] -3.59408922 -15.272087
#> [110,] -5.26517398 -20.921737
#> [111,] -5.58283320 -18.366651
#> [112,] -5.43938876 -8.854396
#> [113,] -5.67648378 -11.178114
#> [114,] -6.62791823 -14.709238
#> [115,] -1.78494732 -12.973720
#> [116,] -5.90930166 -17.129973
#> [117,] -1.35039369 -17.583415
#> [118,] 2.17512316 -19.712052
#> [119,] -6.06594630 -22.120456
#> [120,] -2.62806847 -12.766994
#> [121,] -6.19383247 -17.504033
#> [122,] -1.77931629 -12.908749
#> [123,] -4.20452890 -16.364333
#> [124,] 3.81122198 -19.156231
#> [125,] 3.45901865 -17.704209
#> [126,] -2.55395067 -14.707027
#> [127,] 4.32661428 9.302460
#> [128,] 3.55125411 12.378067
#> [129,] 0.89395106 10.371639
#> [130,] 0.89838297 21.143943
#> [131,] 2.76065573 11.907494
#> [132,] 1.67930440 8.832745
#> [133,] -0.38270469 21.348971
#> [134,] 0.85863540 16.740766
#> [135,] 8.49863627 13.618231
#> [136,] -1.04938880 8.375827
#> [137,] -1.07547859 11.398509
#> [138,] 1.70429350 19.967061
#> [139,] 2.01051415 15.321676
#> [140,] 10.66998053 13.910212
#> [141,] 7.75956216 15.645164
#> [142,] 4.70053431 13.179350
#> [143,] -0.65277375 7.464101
#> [144,] 0.27059489 13.818684
#> [145,] -0.36380370 12.021292
#> [146,] 10.64072567 14.394440
#> [147,] -0.69108196 14.548271
#> [148,] -1.11659119 14.756877
#> [149,] 5.58335271 15.884451
#> [150,] 4.26975023 14.558698
#> [151,] 1.86792239 8.934656
#> [152,] 0.27307075 10.586114
#> [153,] 4.08104284 11.142928
#> [154,] -2.32233591 7.348246
#> [155,] -0.69190002 16.473941
#> [156,] 0.60418060 11.211301
#> [157,] 4.22374359 10.717197
#> [158,] 2.37490967 16.795448
#> [159,] 1.96482540 9.802854
#> [160,] 2.11553996 19.087433
#> [161,] 7.80342701 11.843250
#> [162,] 0.94118026 19.716956
#> [163,] 1.95487302 19.692072
#> [164,] 1.32505351 20.314299
#> [165,] 10.18185933 12.997851
#> [166,] 5.21661097 10.664394
#> [167,] 3.98704252 18.071398
#> [168,] 3.14445111 13.698052
#> [169,] 10.91441050 13.564926
#> [170,] 0.88159907 9.612618
#> [171,] 5.84281816 7.880112
#> [172,] 1.68124486 13.953424
#> [173,] 3.34195603 9.816130
#> [174,] 2.24478832 10.620239
#> [175,] 10.43354508 13.437894
#> [176,] 0.47901253 20.909273
#> [177,] 2.18624895 15.971381
#> [178,] 1.90940467 13.223076
#> [179,] 2.67198192 14.407414
#> [180,] 6.66371618 9.665008
#> [181,] 6.32595704 9.406645
#> [182,] 1.30095632 9.166236
#> [183,] 1.96923776 8.797758
#> [184,] 11.72944842 13.378054
#> [185,] 4.89410761 12.169414
#> [186,] -2.46775175 6.507304
#> [187,] 2.40703540 9.763486
#> [188,] -0.63472642 6.898632
#> [189,] 6.29949186 7.573878
#> [190,] -0.89914612 8.972461
#> [191,] 5.58473180 14.112808
#> [192,] 2.35743619 17.176222
#> [193,] 7.91048565 14.334874
#> [194,] -0.77330448 10.939591
#> [195,] 1.18694517 21.625419
#> [196,] 7.06793204 17.185447
#> [197,] 1.87949483 20.631623
#> [198,] -1.54477466 6.743740
#> [199,] -0.71313701 10.398613
#> [200,] 2.65704000 8.511717
#> [201,] -1.95943653 6.538088
#> [202,] 1.04924563 6.605924
#> [203,] 3.14702347 15.344417
#> [204,] 2.19997654 7.944787
#> [205,] -0.78034452 13.865418
#> [206,] 3.59341154 13.837347
#> [207,] -1.57383301 8.485237
#> [208,] 0.80042346 15.835192
#> [209,] 1.15669460 17.341306
#> [210,] -0.49428222 15.261781
#> [211,] 0.23122105 12.068281
#> [212,] 6.94567859 9.476660
#> [213,] 9.85515470 13.503091
#> [214,] 0.30208147 17.582696
#> [215,] 0.63962140 20.364197
#> [216,] 0.51947442 19.806204
#> [217,] 0.18889809 13.043415
#> [218,] -2.10210489 7.761172
#> [219,] 7.27033843 9.461845
#> [220,] 4.95401765 12.393194
#> [221,] 2.50690338 20.426533
#> [222,] 2.49125987 16.206675
#> [223,] -0.28047052 20.590517
#> [224,] 2.37018949 14.300859
#> [225,] 9.62839027 13.667170
#> [226,] 2.54687115 18.882703
#> [227,] 3.98973577 18.304306
#> [228,] 10.63814466 13.760826
#> [229,] -0.94208912 8.445567
#> [230,] 0.34124234 20.925540
#> [231,] -0.15524476 7.714580
#> [232,] -1.90095661 7.663169
#> [233,] 1.87419706 12.291085
#> [234,] 6.28800151 18.131116
#> [235,] 2.94324543 18.844392
#> [236,] 0.45689256 19.878165
#> [237,] 0.10156675 21.323081
#> [238,] 7.35623934 16.939951
#> [239,] -0.01235342 18.663653
#> [240,] 0.02047117 11.355523
#> [241,] 2.39359094 5.443848
#> [242,] 8.96982003 13.362543
#> [243,] 0.07357717 17.257444
#> [244,] -1.92305115 7.295954
#> [245,] 0.99187797 15.380983
#> [246,] 5.72181134 7.899151
#> [247,] 6.43836206 12.247776
#> [248,] 6.21097842 7.901787
#> [249,] 1.71325100 14.824886
#> [250,] 3.44211016 10.410924
#> [251,] 4.99720863 15.778052
#> [252,] 9.19307982 13.659862
#> [253,] 6.12098466 11.312892
#> [254,] -0.14925350 16.026346
#> [255,] -0.24790121 16.203304
#> [256,] -1.03436781 9.563012
#> [257,] 6.69841934 14.245501
#> [258,] -1.59981716 16.089695
#> [259,] 1.74210645 8.199994
#> [260,] 4.76469331 18.144114
#> [261,] 11.37923260 13.467096
#> [262,] 4.87127506 10.649697
#> [263,] 11.44626559 13.216537
#> [264,] 4.50848503 13.089912
#> [265,] 11.95392327 13.385864
#> [266,] 0.09724124 16.734376
#> [267,] 4.69809783 11.392126
#> [268,] 7.87345311 15.090532
#> [269,] -1.95102846 5.973187
#> [270,] 1.43597458 14.252112
#> [271,] -0.43944230 11.286921
#> [272,] -0.18465652 8.987304
#> [273,] 2.09843114 19.312023
#> [274,] 3.81153105 15.607996
#> [275,] 0.74531172 8.121830
#> [276,] 0.35260939 7.947705
#> [277,] 4.15086206 10.030675
#> [278,] 1.82589937 10.477651
#> [279,] -1.40151200 11.246381
#> [280,] -2.36801966 6.691952
#>
#> $costs
#> [1] 1.268470e-03 5.616187e-04 1.280911e-03 1.543918e-03 5.297053e-04
#> [6] 1.264030e-03 2.212517e-05 -5.070735e-05 -2.787540e-05 1.472186e-04
#> [11] 2.618372e-03 3.562176e-04 1.427522e-03 -3.948765e-05 1.989866e-03
#> [16] 6.078647e-04 1.671381e-04 1.414691e-03 1.518803e-03 1.191712e-03
#> [21] 1.316990e-03 9.211599e-04 1.806888e-04 2.143729e-03 7.777222e-04
#> [26] 6.356404e-04 5.844939e-04 8.549787e-04 5.088371e-05 9.403421e-04
#> [31] -4.307464e-05 4.518142e-04 9.907933e-04 4.139949e-04 3.449080e-03
#> [36] 3.331611e-04 3.442969e-04 5.550483e-04 4.019635e-03 4.128927e-03
#> [41] 1.917445e-03 7.488208e-04 -6.161200e-04 8.943485e-04 4.770052e-04
#> [46] 5.072563e-04 8.450649e-04 1.349513e-03 9.747629e-04 2.786994e-04
#> [51] 7.435445e-04 1.036477e-03 -5.919668e-05 6.850019e-04 1.319361e-03
#> [56] 2.235547e-03 8.137100e-04 1.462533e-03 5.274244e-04 3.779713e-04
#> [61] 1.026156e-04 9.523088e-04 -3.892156e-05 1.311990e-03 1.533330e-03
#> [66] 1.456337e-03 8.240417e-04 1.330233e-03 5.657046e-04 6.297338e-05
#> [71] 5.158202e-05 2.907818e-04 9.636466e-04 2.068097e-04 3.385838e-04
#> [76] 1.727137e-04 5.573801e-04 -5.150117e-04 1.208687e-03 1.765724e-04
#> [81] 9.037489e-04 4.600203e-04 1.430012e-03 3.026954e-04 7.055880e-04
#> [86] 2.151177e-03 -5.657831e-05 3.255948e-04 4.122651e-04 2.596556e-03
#> [91] -1.710423e-04 9.304122e-04 8.666966e-04 6.828857e-04 1.313820e-03
#> [96] 7.728343e-04 2.211745e-04 1.645620e-03 1.513019e-03 -7.067259e-05
#> [101] 1.136322e-04 -2.454287e-04 2.373120e-03 5.809140e-04 7.127932e-04
#> [106] 1.719441e-04 8.710232e-04 3.531259e-04 1.510294e-03 -1.488270e-05
#> [111] 3.572561e-04 6.338157e-04 6.450006e-04 2.188081e-04 1.651620e-03
#> [116] 3.118557e-04 1.985967e-03 3.544158e-04 1.654024e-04 7.515073e-04
#> [121] 5.327760e-04 1.057330e-03 3.820033e-04 1.162463e-04 1.727576e-03
#> [126] 1.090915e-03 1.366486e-03 1.274675e-03 3.041580e-04 2.947353e-05
#> [131] 8.029760e-04 6.765267e-04 2.060047e-05 1.223667e-03 8.391803e-04
#> [136] 8.452170e-04 1.138363e-03 2.533533e-05 3.338094e-03 3.599084e-04
#> [141] 2.598799e-03 8.832708e-04 6.707527e-04 6.957703e-04 1.453426e-03
#> [146] 8.259180e-04 1.334646e-03 9.345302e-04 2.151229e-04 3.760175e-03
#> [151] 7.510244e-04 1.945072e-03 1.723623e-03 5.553067e-06 6.043446e-04
#> [156] 1.952236e-04 1.344759e-03 1.779758e-03 1.147047e-03 9.838382e-04
#> [161] 2.642942e-03 8.096634e-04 7.122868e-04 1.520361e-04 1.571532e-04
#> [166] 6.441100e-04 2.052647e-03 3.333838e-03 2.501761e-04 5.349627e-04
#> [171] 1.286585e-03 2.369820e-03 1.207498e-03 1.198606e-03 2.810520e-04
#> [176] -3.390501e-04 7.723888e-04 2.651455e-03 1.247522e-03 3.887590e-03
#> [181] 1.202196e-03 1.713496e-03 1.475077e-03 6.914219e-04 3.580793e-04
#> [186] 5.764043e-05 5.974948e-04 4.653095e-04 8.850869e-04 1.459536e-03
#> [191] 7.605895e-04 1.559027e-03 2.986817e-03 1.494508e-03 7.002981e-05
#> [196] 2.170326e-03 6.020494e-04 2.284491e-04 1.564666e-03 -3.839084e-05
#> [201] 1.158894e-04 3.562691e-03 1.675291e-03 7.268457e-04 2.230405e-03
#> [206] 2.606000e-03 6.548716e-04 -6.398448e-04 5.071388e-04 1.114841e-03
#> [211] 2.186676e-03 4.001644e-03 7.219480e-04 1.282926e-03 5.356154e-05
#> [216] 9.442128e-05 1.628873e-03 1.212121e-04 9.514490e-04 6.455522e-04
#> [221] 1.218176e-06 1.451463e-03 3.001422e-04 2.049226e-03 6.679158e-04
#> [226] 1.526321e-03 1.933960e-03 2.514584e-04 6.756935e-04 -1.164116e-04
#> [231] 1.124682e-03 6.317509e-05 3.064103e-03 1.989443e-03 4.122464e-04
#> [236] -2.024155e-04 -3.825223e-05 1.007911e-03 1.328614e-03 1.023608e-03
#> [241] 3.846256e-03 1.858672e-03 6.525372e-04 6.171062e-05 7.846191e-04
#> [246] 1.559735e-03 1.412168e-03 1.830645e-03 1.822230e-03 1.300523e-03
#> [251] 2.072767e-03 1.447324e-03 1.041612e-03 1.325850e-03 7.691127e-04
#> [256] 1.275887e-03 4.685111e-03 1.619327e-03 1.078641e-03 9.539217e-04
#> [261] 4.823906e-04 1.078623e-03 2.420004e-04 2.334402e-03 9.540422e-04
#> [266] 1.146610e-03 1.891390e-03 1.297935e-03 1.758168e-04 1.673831e-03
#> [271] 1.482305e-03 9.084798e-04 9.889593e-04 9.792318e-04 8.845710e-04
#> [276] 3.752094e-04 1.533062e-03 1.337785e-03 6.731742e-04 3.201092e-05
#>
#> $itercosts
#> [1] 49.0381974 48.1091295 47.9682004 47.8326895 47.8612154 0.3607616
#> [7] 0.3122722 0.2964914 0.2918155 0.2897631 0.2859687 0.2844417
#> [13] 0.2850085 0.2839697 0.2838602 0.2833419 0.2821053 0.2816678
#> [19] 0.2819076 0.2811398
#>
#> $origD
#> [1] 50
#>
#> $perplexity
#> [1] 30
#>
#> $theta
#> [1] 0.5
#>
#> $max_iter
#> [1] 1000
#>
#> $stop_lying_iter
#> [1] 250
#>
#> $mom_switch_iter
#> [1] 250
#>
#> $momentum
#> [1] 0.5
#>
#> $final_momentum
#> [1] 0.8
#>
#> $eta
#> [1] 200
#>
#> $exaggeration_factor
#> [1] 12
#>
#> attr(,"class")
#> [1] "Rtsne" "list"
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 30.538 0.53153 157.14 0.001 ***
#> Residual 277 26.915 0.46847
#> Total 279 57.453 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.2.1.1.3 NMDS
beta_dispersion(enterotype, dist = "bray", method = "NMDS", group = "SeqTech",
color_vector = c("#777711", "#117777", "#DD7788"),
legend_title = "Sequencing tech without boxplots", lwd = 2,
font = 2, draw = "polygon", text = T, permanova = T,
y.intersp = 0.7,
boxplot = F, where = "bottom")
#> Run 0 stress 0.1380077
#> Run 1 stress 0.1562307
#> Run 2 stress 0.1515837
#> Run 3 stress 0.160802
#> Run 4 stress 0.155239
#> Run 5 stress 0.1498397
#> Run 6 stress 0.1523578
#> Run 7 stress 0.1557621
#> Run 8 stress 0.1567377
#> Run 9 stress 0.1579532
#> Run 10 stress 0.1590286
#> Run 11 stress 0.1564627
#> Run 12 stress 0.1500833
#> Run 13 stress 0.1588504
#> Run 14 stress 0.1521323
#> Run 15 stress 0.1605488
#> Run 16 stress 0.1589632
#> Run 17 stress 0.1525489
#> Run 18 stress 0.1563119
#> Run 19 stress 0.1439898
#> Run 20 stress 0.1514314
#> *** Best solution was not repeated -- monoMDS stopping criteria:
#> 14: stress ratio > sratmax
#> 6: scale factor of the gradient < sfgrmin
#> $reduction
#>
#> Call:
#> metaMDS(comm = otu)
#>
#> global Multidimensional Scaling using monoMDS
#>
#> Data: otu
#> Distance: bray
#>
#> Dimensions: 2
#> Stress: 0.1380077
#> Stress type 1, weak ties
#> Best solution was not repeated after 20 tries
#> The best solution was from try 0 (metric scaling or null solution)
#> Scaling: centring, PC rotation, halfchange scaling
#> Species: expanded scores based on 'otu'
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 30.538 0.53153 157.14 0.001 ***
#> Residual 277 26.915 0.46847
#> Total 279 57.453 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.2.1.1.4 DCA
beta_dispersion(enterotype, dist = "bray", method = "DCA", group = "SeqTech",
color_vector = c("#777711", "#117777", "#DD7788"),
legend_title = "Sequencing tech without boxplots", lwd = 2,
font = 2, draw = "polygon", text = T, permanova = T,
y.intersp = 0.7,
boxplot = F, where = "bottom")
#> $reduction
#>
#> Call:
#> decorana(veg = as(otu_table(reverseASV(physeq)), "matrix"))
#>
#> Detrended correspondence analysis with 26 segments.
#> Rescaling of axes with 4 iterations.
#> Total inertia (scaled Chi-square): 3.8641
#>
#> DCA1 DCA2 DCA3 DCA4
#> Eigenvalues 0.5549 0.2483 0.2753 0.1670
#> Additive Eigenvalues 0.5549 0.2133 0.2613 0.1344
#> Decorana values 0.5997 0.2398 0.1972 0.1521
#> Axis lengths 3.7145 2.4516 3.2989 2.2386
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 30.538 0.53153 157.14 0.001 ***
#> Residual 277 26.915 0.46847
#> Total 279 57.453 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.2.1.1.5 PCA
beta_dispersion(enterotype, dist = "bray", method = "PCA", group = "SeqTech",
color_vector = c("#777711", "#117777", "#DD7788"),
legend_title = "Sequencing tech without boxplots", lwd = 2,
font = 2, draw = "polygon", text = T, permanova = T,
y.intersp = 0.7,
boxplot = F, where = "bottom")
#> Warning in plot.window(...): "permanova" n'est pas un paramètre graphique
#> Warning in plot.window(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy, type, ...): "permanova" n'est pas un paramètre graphique
#> Warning in plot.xy(xy, type, ...): "boxplot" n'est pas un paramètre graphique
#> Warning in title(...): "permanova" n'est pas un paramètre graphique
#> Warning in title(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy.coords(x, y), type = type, ...): "permanova" n'est pas un
#> paramètre graphique
#> Warning in plot.xy(xy.coords(x, y), type = type, ...): "boxplot" n'est pas un
#> paramètre graphique
#> $reduction
#>
#> Call: rda(X = otu, scale = T)
#>
#> Inertia Rank
#> Total 553
#> Unconstrained 553 270
#>
#> Inertia is correlations
#>
#> Eigenvalues for unconstrained axes:
#> PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
#> 332.5 14.4 13.3 8.7 7.2 5.5 4.8 4.6
#> (Showing 8 of 270 unconstrained eigenvalues)
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 30.538 0.53153 157.14 0.001 ***
#> Residual 277 26.915 0.46847
#> Total 279 57.453 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.2.1.1.6 CA
beta_dispersion(enterotype, dist = "bray", method = "CA", group = "SeqTech",
color_vector = c("#777711", "#117777", "#DD7788"),
legend_title = "Sequencing tech without boxplots", lwd = 2,
font = 2, draw = "polygon", text = T, permanova = T,
y.intersp = 0.7,
boxplot = F, where = "bottom")
#> $reduction
#>
#> Call: cca(X = as(otu_table(reverseASV(physeq)), "matrix"))
#>
#> Inertia Rank
#> Total 3.864
#> Unconstrained 3.864 263
#>
#> Inertia is scaled Chi-square
#>
#> Eigenvalues for unconstrained axes:
#> CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
#> 0.5997 0.4023 0.2811 0.2569 0.2372 0.2301 0.1737 0.1567
#> (Showing 8 of 263 unconstrained eigenvalues)
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 30.538 0.53153 157.14 0.001 ***
#> Residual 277 26.915 0.46847
#> Total 279 57.453 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.2.1.2 Constrained models
Constrained analysis are multiple regression of the unconstrained
approaches. They allow to fit the ecological data or sample data to the
community data. Alternatively, you can fit other omic data to your beta
diversity analysis. We have two different classes of constrained
analysis :
rdais a regression of a PCAccais a regression of a CAdbRDAis a RDA based on a dissimilarity matrix
The function will return a plot, same asbeta_dispersion, and also the result of the constrained analysis.
Vegan authors have implemented a very nice feature if you want to
make your own representation instead of using mine. You can set use
scores(res, tidy=T) to return a dataframe compatible with
ggplot2.
3.1.2.1.2.1 CCA
res= constrained_beta_dispersion(enterotype,
model= "SeqTech+Gender+Nationality+Age+ClinicalStatus",
group="Enterotype", method = "CCA",
boxplot =T,
text=T,
color_vector = tol21rainbow)
#>
#> Some constraints or conditions were aliased because they were redundant. This
#> can happen if terms are constant or linearly dependent (collinear):
#> 'SeqTech.ThisVarHasOnly1Level', 'ClinicalStatuselderly'
#> Warning in plot.window(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy, type, ...): "boxplot" n'est pas un paramètre graphique
#> Warning in title(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy.coords(x, y), type = type, ...): "boxplot" n'est pas un
#> paramètre graphique
3.1.2.1.2.2 RDA
res= constrained_beta_dispersion(enterotype,
model= "SeqTech+Gender+Nationality+Age+ClinicalStatus",
group="Enterotype", method = "RDA",
boxplot =T,
text=T,
color_vector = tol21rainbow)
#>
#> Some constraints or conditions were aliased because they were redundant. This
#> can happen if terms are constant or linearly dependent (collinear):
#> 'SeqTech.ThisVarHasOnly1Level', 'ClinicalStatuselderly'
#> Warning in plot.window(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy, type, ...): "boxplot" n'est pas un paramètre graphique
#> Warning in title(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy.coords(x, y), type = type, ...): "boxplot" n'est pas un
#> paramètre graphique
3.1.2.1.2.3 dbRDA
res= constrained_beta_dispersion(enterotype,
model= "SeqTech+Gender+Nationality+Age+ClinicalStatus",
group="Enterotype", method = "dbRDA",
boxplot =T,
text=T,
color_vector = tol21rainbow)
#>
#> Some constraints or conditions were aliased because they were redundant. This
#> can happen if terms are constant or linearly dependent (collinear):
#> 'SeqTech.ThisVarHasOnly1Level', 'ClinicalStatuselderly'
#> Warning in plot.window(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy, type, ...): "boxplot" n'est pas un paramètre graphique
#> Warning in title(...): "boxplot" n'est pas un paramètre graphique
#> Warning in plot.xy(xy.coords(x, y), type = type, ...): "boxplot" n'est pas un
#> paramètre graphique
You can then use anova statistic test to decipher which
factor or feature is significantly associated with your beta
diversity
anova(res, by="terms")
#> Permutation test for capscale under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#>
#> Model: capscale(formula = as(otu_table(reverseASV(physeq)), "matrix") ~ SeqTech + Gender + Nationality + Age + ClinicalStatus, data = df, distance = dist, na.action = na.exclude)
#> Df SumOfSqs F Pr(>F)
#> Gender 1 0.06650 0.7390 0.637
#> Nationality 5 1.06616 2.3695 0.006 **
#> Age 1 0.43786 4.8657 0.002 **
#> ClinicalStatus 3 0.30763 1.1395 0.277
#> Residual 26 2.33975
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.3 Heatmap based on the phyloseq object
::: {style=“text-align: justify”} This function will perform a top
taxa at a rank of your choosing and create a heatmap with annotations.
For now only one annotation is supported.
The clustering is made using the hclustfunction and a
Ward.D2 method. You can define the distance matrix that you want to use.
It is important to note that the distance is made before any trimming of
the data. This means that the distance matrix is made at the ASV/OTU
level before doing the rank merging and topping, so the clustering will
represent your “true” data instead of a modified dataset. In my
personnal opinion, this is the only way of performing clustering or any
type of generalized analysis : clustering, reducing or else on the
ASV/OTU/MAG/… level and then aggregates for plotting.
If you really want to cluster on other taxonomic level you should use
tax_glom before using this function.
phylo_heatmap(enterotype, top = 30, labels = "SeqTech", taxa_rank = "Genus", factor_to_plot ="Enterotype", split = 3, distance = "bray" )
#> [1] "No phylogenetic tree in this phyloseq object, bray-curtis distance selected."
#> [1] " not reversed"
3.2
3.3 Usage of various functions
The idea behind these functions is : creating a more automatic
pipeline enabling filtering and subseting of the phyloseq
object without having to perform a temporary phyloseq
object and a temporary distance object. With these function you can
directly use the “pipe” introduced by magrittr.
So you can use a subset_samples like the following and
automatically plot your beta diversity without adding too much code.
enterotype %>%
subset_samples(SeqTech=="Illumina") %>%
beta_dispersion(group="Enterotype", color_vector = c("#777711", "#117777", "#DD7788"),
legend_title = "Enterotypes \n with bray" ,
lwd = 2, font = 2, draw = "lines", text = T, permanova = T, y.intersp = 0.7,
where="bottomleft", cex = 3)
#> $reduction
#> Call: vegan::wcmdscale(d = d, eig = T)
#>
#> Inertia Rank
#> Total 2.9338
#> Real 3.4327 41
#> Imaginary -0.4989 43
#>
#> Results have 85 points, 41 axes
#>
#> Eigenvalues:
#> [1] 1.0925 0.9475 0.2273 0.1685 0.1381 0.1072 0.0971 0.0749 0.0671
#> [10] 0.0625 0.0499 0.0485 0.0429 0.0370 0.0292 0.0266 0.0244 0.0226
#> [19] 0.0208 0.0183 0.0180 0.0159 0.0123 0.0116 0.0103 0.0089 0.0078
#> [28] 0.0073 0.0068 0.0058 0.0054 0.0043 0.0039 0.0031 0.0024 0.0021
#> [37] 0.0016 0.0008 0.0007 0.0006 0.0003 -0.0005 -0.0007 -0.0010 -0.0012
#> [46] -0.0014 -0.0016 -0.0019 -0.0023 -0.0027 -0.0031 -0.0032 -0.0035 -0.0037
#> [55] -0.0038 -0.0042 -0.0045 -0.0049 -0.0053 -0.0055 -0.0061 -0.0063 -0.0067
#> [64] -0.0075 -0.0081 -0.0089 -0.0094 -0.0097 -0.0105 -0.0106 -0.0121 -0.0122
#> [73] -0.0134 -0.0152 -0.0158 -0.0165 -0.0188 -0.0205 -0.0221 -0.0260 -0.0292
#> [82] -0.0362 -0.0494 -0.0727
#>
#> Weights: Constant
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(paste0(" as(otu_table(reverseASV(physeq)), 'matrix') ~", group)), data = as(sample_data(physeq), "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 2 1.0638 0.3626 23.324 0.001 ***
#> Residual 82 1.8700 0.6374
#> Total 84 2.9337 1.0000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Additionally, if you want to go further you can also serialized the
code with a for loop or a lapply or even a
parallel approach using mclapply or
doParallel.
layout(matrix(c(1,2,3),
nrow = 1,
ncol = 3,
byrow = TRUE))
for(i in c("Illumina", "Sanger", "Pyro454")){
enterotype %>%
subset_samples(SeqTech==i & !is.na(Enterotype))%>%
beta_diversity(dist="bray", method="PCoA", group="Enterotype",
color_vector =c("#777711", "#117777", "#DD7788"),
factor_to_plot = paste("Enterotypes using \n",i, "sequencing"), lwd = 2, cpoint = 2)
}
3.4 Differential abundance testing
Differential abundance testing is quite complicated because of the
number of different statistical approaches existing in the literature. I
personally use ALDEx2 and SIAMCAT a lot. The first because it is usually
highly regarded and the second for the overall easiness of the package.
For now differential_abundance implement only
ALDEx2 approach.
The function will return two datasets (all taxa and one with only the significant taxa) and two plots representing the taxa significantly represented in one of the two conditions. This function only covers two by two analysis.
PROBLEM IN ALDEX2 CODE
data("GlobalPatterns")
tmp= GlobalPatterns %>%
subset_samples(SampleType=="Feces" | SampleType=="Soil")%>%
subset_taxa(Genus!="NA")%>%
tax_glom("Genus")
taxa_names(tmp)= paste0(tax_table(tmp)[,'Genus'], 1:length(tax_table(tmp)[,"Genus"]))
res= tmp %>%
differential_abundance( group="SampleType", col1 = "brown", col2="darkgreen", plot = T)
3.4.1 Results
head(res$all_features)
#> NULL3.4.2 Barplot results
res$barplot
#> NULL3.4.3 Volcano plot results
res$volcano
#> NULLTBD
3.5 Model testing
Last but not least : machine learning. Machine learning is used in
general to predict outcome or predict class assignation between health
and disease status. For this purpose we developed wrapper functions to
screen models more easily. Most of the code is actually generated using
caret [https://topepo.github.io/caret/] package.
For now the functions implement randomForest, glmnet and plsda models, other model might work but I didn’t test them yet.
3.5.1 Screen models on your raw data
This function accepts either
# res = screen_models(enterotype, model="glmnet", cores = 1, number = 3, repeats = 3)3.6 Non phyloseq object oriented functions
I also created functions to make classical analysis on non phyloseq objects. A classical type of data are metabolomics or flow cytometry datasets. These datasets can easily make use of dimension reduction, analyse of variance, model testing.
3.6.1 Reduction of dimensions on non phyloseq objects
plot_reduction()
plot_constrained_reduction()The first function is an equivalent of and the second one is an equivalent of . Make sure to use data.frame with samples as rows.
data(metabolomic)
metabolomic |>
plot_reduction(clinical_data = 1:4, group = "sex", method = "PCA", text = T, stat = "permanova", type = "arrows" )
#> $reduction
#>
#> Call: rda(X = as(mat[, -clinical_data], "matrix"), scale = scale)
#>
#> Inertia Rank
#> Total 213
#> Unconstrained 213 98
#>
#> Inertia is correlations
#>
#> Eigenvalues for unconstrained axes:
#> PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
#> 27.941 14.341 12.712 11.406 9.874 9.110 7.954 6.072
#> (Showing 8 of 98 unconstrained eigenvalues)
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = as.formula(as.formula(paste0("mat[, -clinical_data] ~", group))), data = as(mat, "data.frame"), permutations = 999, method = dist, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 1 0.0858 0.02647 2.6377 0.021 *
#> Residual 97 3.1567 0.97353
#> Total 98 3.2425 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
metabolomic |>
plot_constrained_reduction(clinical_data = 1:4, model="birth_type+breastfeeding+sex", group = "birth_type")
#> $model
#>
#> Call: cca(formula = as(mat[, -clinical_data], "matrix") ~ birth_type +
#> breastfeeding + sex, data = mat[, clinical_data], na.action = na.exclude,
#> scale = scale)
#>
#> Inertia Proportion Rank
#> Total 1.52003 1.00000
#> Constrained 0.05714 0.03759 3
#> Unconstrained 1.46289 0.96241 95
#>
#> Inertia is scaled Chi-square
#>
#> Eigenvalues for constrained axes:
#> CCA1 CCA2 CCA3
#> 0.029818 0.019580 0.007746
#>
#> Eigenvalues for unconstrained axes:
#> CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
#> 0.20095 0.18072 0.12443 0.09841 0.07894 0.06957 0.06262 0.05224
#> (Showing 8 of 95 unconstrained eigenvalues)
#>
#>
#> $stat
#> Permutation test for adonis under reduced model
#> Marginal effects of terms
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = mod, data = as(mat, "data.frame"), permutations = 999, method = dist, by = by, na.action = na.exclude)
#> Df SumOfSqs R2 F Pr(>F)
#> birth_type 1 0.1916 0.01719 1.7004 0.100 .
#> breastfeeding 1 0.0449 0.00403 0.3988 0.960
#> sex 1 0.1972 0.01770 1.7502 0.079 .
#> Residual 95 10.7031 0.96063
#> Total 98 11.1417 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.6.2 Analysis of variance
Here is an important part of any analysis : finding which factors explains the most variance. The principle is that we will get the variance for each features and the variance for each group. The group variance will be summed for the given factor and then we will use 1-variance.factor/variance.total, which will give us the variance for each factors.
On top of the variance, the function will calculate p.values using kurskal tests or wilcoxon test depending of the number of factors and a FDR correction. For now the function only supports non parametric tests.
The function can take quite some time if you have a lot of samples, features and factors, you can use multi-threading here.
variance = metabolomic |>
dplyr::select(!child_id) |>
calculate_variance(clinical_data = 1:3, cores = 1)
#> [1] "birth_type"
#> [1] "breastfeeding"
#> [1] "sex"
#> p valuye now
#> Using features as id variables
#> Using features as id variables
#> Using features as id variablesThe function will produce a list of data.frame
4 IgA seq analysis pipeline
You’ve performed all your sortings and sequencing, you now have three
samples coming from a single individual.
We created a pipeline analysis where you will use the
neg1 (9/10 of the neg fraction) and
neg2 (1/10 of the neg fraction) dispersion (centered
and reduced) to create a normal dispersion, using a Z approach we will
have a Z score for the pos/neg1 based on the standard
deviation of neg1/neg2.
Here we can see what the analysis will look like:
The technical dispersion is assessed using
neg1/neg2for the log2 ratio and theneg1*neg2abundance for log10 (black dot)The biological dispersion is assessed
pos/neg1for the log2 ratio and thepos*neg1abundance for log10 (orange dot)
We will then take windows of X ASV (for example 20) to create n
Gaussian curve and n …
The pipeline is based on three main functions that will call for other
functions : seq_table, slide_z and collapse_IgAseq.
4.1 Make the seq_table list
First the seq_table function will take your phyloseq object and transform it to a list of data frames, one data frame for each samples coming from a single individual. For this function you will need to give physeq, sample_name corresponding to the name identifying the individual from which the sorted samples came, sorting_names the column where we find the samples such as : “sample1_pos”, “sample1_neg1”, “sample1_neg2”. Then the cols_to_keep that need to stay for now on “all”, it will collapse your sample_data in one column to allow you get it back later on.
The function will tell you if there is some samples are alone, if you have duplicated samples you need to sort them out or the rest of the pipeline will block. You can take a look at the architecture of the new object, you will find the ASV sequence as rownames, the taxonomy collapsed with “#” separator, the three samples having their own column and the sample_data collapse using also “#” separator. The function will only take the ASV that are present in the samples.
data("igaseq")
igaseq = transform_sample_counts(igaseq, function(x)x/sum(x))
sample_names(igaseq)= sample_data(igaseq)$sample_sort
seq.tab= seq_table(igaseq, sample_name = "sample_origin", sorting_names = "sample_sort", cols_to_keep = "all" )
DT::datatable(seq.tab$MO101, rownames = F)
The colnames will have the sample_names as given in the otu_table(), check that they correspond to your pos, neg1 and neg2.
Run the main function Now we will run the main function : slide_z. This function will take you seq_table object and run the Z function for each samples. If you are running this function for the first time use plot=T, if you already made the plots let it as FALSE it will make the loop a lot faster.
4.2 Deal with the zero values
In this approach we will use log2 ratio and log10 of abundances. As you know log2(0/x) or log2(x/0) can’t be performed, so we need a way to deal with the zeros. We came up with two approach :
remove all ASV with a zero value
replace 0 by a random number between 0 and the min value found in one of the three samples
replace 0 by the minimum count in each samples, this probably the worst thing to do
In sample with few ASV, i.e meconiums, I strongly recommend using the random_generation while in adults samples the no_zero approach is performing better. For more complex samples the decision is up to you, you can use the function neg_dipsersion to visualize the two outcomes.
neg_dispersion(seq.tab[[6]], positive_sorted_sample = "pos", negative_sorted_sample = "neg1", second_negative_sample = "neg2", type = "superposed")
neg_dispersion(seq.tab[[6]], positive_sorted_sample = "pos", negative_sorted_sample = "neg1", second_negative_sample = "neg2",type = "facet all three")# run the following if you want every samples, make sure to change the number of cores
# pdf("test.pdf", width = 10, height = 7)
# mclapply(seq, neg_dispersion, mc.cores = 6, type="superposed")
# dev.off()4.3 Run the slide_z function
The first step will be to create log2 ratios and log10 abundance
(log10 abundance of pos * neg1 for
example) for each ASV between the pos and the
neg1 (log_2_ratio - log10_abundance) and between the
neg1 and neg2 (log2_neg_ratio
- log10_neg_abundance). This will be done by the function
log_ratio called by the slide_z
function.
The function will also create an ellipse of confidence interval of your
choosing, default being
confidence_interval=c(0.95,0.99, 0.999), this is done by
the function ellipse_me also called by the `slide_z`
function.
The output will be a list of S4 objects containing the following slots :
ig_seq_all : containing all the samples and all the ASV
ig_up : containing all the samples and all the ASV that significantly enriched in the IgA positive fraction
ig_down : containing all the samples and all the ASV that are significantly enriched in the IgA negative fraction
In each slot you will find the following columns :
taxonomy
sample_id
new : the sample_data collapsed
pos : positive fraction abundance
neg1 : negative (9/10) fraction abundance
neg2 : negative (1/10) fraction abundance
log10_abundance = log10(pos * neg1)
log2_ratio = log2(pos / neg1)
log10_neg_abundance = log10(neg1 * neg2)
log2_neg_ratio = log2(neg1 / neg2)
taxa = tax_table collapsed
SlideNorm = the normalized dispersion for each ASV
score = IgAseq score
ellipse_level = the level of confidence
IgA_seq=list()
system.time(for(i in names(seq.tab)){
print(i)
IgA_seq[[i]]= slide_z(seq.tab[[i]], positive_sorted_sample = "pos", negative_sorted_sample = "neg1", second_negative_sample = "neg2", deltaX = 30, slide_version = "slide_z_modern", alpha = 0.05, plot = F, zero_treatment = "random generation")
})4.4 Collapse the list of IgAseq into a single S4 object
We now need to collapse the list of IgA_seq objects into a single
one, just use collapse_IgAseq and separate the columns that
were pasted together.
IgA_seq = collapse_IgAseq(IgA_seq)
# DT:: datatable(IgA_seq@ig_seq_all, rownames = F)
# View(IgA_seq)
IgA_all= IgA_seq@ig_seq_all %>%
separate(taxonomy, into=c("Reign","Phylum", "Order","Class","Family", "Genus","Species","ASV", "rest"), sep="#") %>%
separate(col = new, into=colnames(sample_data(igaseq)),sep= "#" )
IgA_all$alpha= ifelse(IgA_all$score>1.96, "positively significative", ifelse(IgA_all$score< -1.96, "negatively significative", "not significative"))
dim(IgA_all)4.5 Plot like Gordon IgA seq
For the example we will plot the IgAseq data like Gordon’s paper. We first make a wilcoxon test to decipher which genera are significantly different from zero. Then we plot as balloon plot using ggplot.
library(rstatix)
tmp= IgA_all %>%
group_by(Genus, donor)%>%
mutate(n=n())%>%
filter(n>5 , Genus!="NA")%>%
wilcox_test(score~1, mu=0, alternative ="two.sided", detailed = T)
tmp2= IgA_all %>%
group_by(Genus, donor)%>%
mutate(n=n())%>%
filter(n>5, Genus!="NA")%>%
dplyr::select(Genus, score, donor)%>%
mutate(mean_score= median(score))%>%
left_join(tmp)
tmp2$mean_score[tmp2$mean_score <= (-5)]= (-5)
alpha= ifelse(tmp2$p<0.05, -log10(tmp2$p), 0.5)
tmp2$donor= factor(tmp2$donor, levels = c("child_delivery","child_2_months","child_24_months", "mother_24_months"))
tmp2= tmp2%>%
ungroup()%>%
select(p, score, mean_score, Genus, donor)%>%
mutate(alpha= ifelse(tmp2$p<0.05, -log10(tmp2$p), 0),
alpha2= ifelse(tmp2$mean_score<0, -alpha, alpha),
mean_score2= rescale(c(abs(mean_score)), to=c(0,5)))
tmp2 %>%
ggplot(aes(0, Genus, fill=alpha2, size=mean_score2)) +
geom_point(shape=21) +
scale_fill_gradient2(low = "olivedrab4", mid = "white", high = "brown", midpoint = 0, guide=F)+
scale_size_continuous(breaks=c(0,3,3,4,4,5,5),labels=c(-5,-4,-3,0,3,4,5),range = c(0,5))+
guides(size = guide_legend( override.aes = list(fill =colorRampPalette(c("darkgreen", "white", "brown"))(7),
size=c(5,4,3,1,3,4,5)), nrow=1,
direction = "horizontal",
title.position = "top",
label.position = "bottom",
label.hjust = 0.5,
label.vjust = 1))+
labs(fill="", x="Age", size="IgAseq median score")+
facet_grid(Phylum~donor, scales="free", space = "free", labeller = labeller(donor=fac))+
theme(axis.text.y = element_text(size=8, face="bold"),
axis.text.x=element_blank(),
axis.ticks.x = element_blank(),
strip.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = 'bottom',
legend.title = element_text(size=10, face="bold"),
legend.text = element_text(size=10, face="bold"),
legend.box = "vertical",
legend.box.background = element_rect(colour = "black"),
strip.background = element_blank(),
strip.text = element_text(size=12, face="bold"),
panel.grid.major.y = element_line(linetype = 2, size=.05)
)